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Details of Grant 

EPSRC Reference: GR/S34137/01
Title: Novel Sequential Monte Carlo Methods for Optimal Estimation in Dynamic Stochastic Models
Principal Investigator: Young, Professor SJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Engineering
Organisation: University of Cambridge
Scheme: First Grant Scheme Pre-FEC
Starts: 02 March 2004 Ends: 01 March 2007 Value (£): 125,203
EPSRC Research Topic Classifications:
Control Engineering Digital Signal Processing
Modelling & simul. of IT sys.
EPSRC Industrial Sector Classifications:
Communications
Related Grants:
Panel History:  
Summary on Grant Application Form
Stochastic models are routinely used in many fields of engineering, finance, operation research and science. Except for several special cases such as the linear Gaussian model, optimal estimation problems do not admit any closed-form solution. Realistic dynamic models Involve complex, time-varying parameter stochastic structures, often with non-linear and non-Gaussian components, and are typically not amenable to analysis without resort to approximations. Examples include tracking an aircraft using radar measurements or estimating the volatility of financial instruments using stock market data. For this class of problems, the Sequential Monte Carlo (SMC) approach, aka particle filtering, shows great promise. The potential of the SMC methods has been recently demonstrated by researchers in many different application areas. In the mean time, however, many fundamental problems remain open regarding their design, Implementation and performance analysis. Moreover, new challenges arise from applications, where novel SMC methods must be developed to cope with the structures and the complexities of specific problems, In this project, we will focus on a number of important Issues which are vital for the SMC methodology to prevail.
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Organisation Website: http://www.cam.ac.uk